Predicting the magnetic measurements of first- and second-order phase transition magnetocaloric materials with artificial neural networks

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Pinto, RMC (Author) Other; Belo, JH (Author) FCUP; araujo, j. p. (Author) FCUP; Silva, DJ (Author) Other

Journal

Title: Journal of Magnetism and Magnetic Materials

Vol. 562

ISSN: 0304-8853

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

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Other information

Authenticus ID: P-00X-2SA

DOI: 10.1016/j.jmmm.2022.169706

Abstract (EN): Due to the large innovative applications involving magnetocaloric materials, such as magnetic cooling devices and thermomagnetic engines, the number of studies on their characterization have been increasing over the past two decades. Although indispensable, magnetic measurements are considerably time-consuming, which sometimes hinder depth studies on the subject. We propose a method that uses artificial neural networks to predict magnetic measurements of magnetocaloric materials with first- and second-order phase transitions, aiming to reduce the required number of measured field dependent magnetization curves M(H) in standard studies. We validate the method for magnetic measurements of the first- and second-order phase transition materials Gd5Ge4 and Gd5Si2.4 Ge-1.(6), respectively. The application of the proposed method results in very high accuracies, with r(2) > 0.98, when comparing the prediction to measured M(H) curves. The entropy change vs temperature curves upon the application of 5 T magnetic fields are also predicted.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5